Leveraging R&D to advance digital modelling practice in Australian construction

This paper describes a lead project currently underway through Australia’s Sustainable Built Environment National Research Centre, evaluating impacts, diffusion mechanisms and uptake of R&D in the Australian building and construction industry. Building on a retrospective analysis of R&D trends and industry outcomes, a future-focused industry roadmap will be developed to inform R&D policies more attuned to future industry needs to improve investment effectiveness. In particular, this research will evaluate national R&D efforts to develop, test and implement advanced digital modelling technologies into the design/construction/asset management cycle. This research will build new understandings and knowledge relevant to R&D funding strategies, research team formation and management (with involvement from public and private sectors, and research and knowledge institutions), dissemination of outcomes and uptake. This is critical due to the disaggregated nature of the industry, intense competition, limited R&D investment; and new challenges (e.g. digital modelling, integrated project delivery, and the demand for packaged services). The evaluation of leading Australian and international efforts to integrate advanced digital modelling technologies into the design/construction/asset management cycle will be undertaken as one of three case studies. Employing the recently released Australian Guidelines for Digital Modelling developed with buildingSMART (International Alliance for Interoperability) and the Australian Institute of Architects, technical and business benefits across the supply chain will be highlighted as drivers for more integrated R&D efforts.

Probabilistic subgroup identification using Bayesian finite mixture modelling : a case study in Parkinson’s disease phenotype identification

This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

Deterministic diffraction loss modelling for novel broadband communication in rural environments

This paper presents a deterministic modelling approach to predict diffraction loss for an innovative Multi-User-Single-Antenna (MUSA) MIMO technology, proposed for rural Australian environments. In order to calculate diffraction loss, six receivers have been considered around an access point in a selected rural environment. Generated terrain profiles for six receivers are presented in this paper. Simulation results using classical diffraction models and diffraction theory are also presented by accounting the rural Australian terrain data. Results show that in an area of 900 m by 900 m surrounding the receivers, path loss due to diffraction can range between 5 dB and 35 dB. Diffraction loss maps can contribute to determine the optimal location for receivers of MUSA-MIMO systems in rural areas.

Prognostic modelling options for remaining useful life estimation by industry

Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models.

A prototype augmented reality collaborative process modelling tool

Identifying, modelling and documenting business processes usually requires the collaboration of many stakeholders that may be spread across companies in inter-organizational business settings. While there are many process modelling tools available, the support they provide for remote collaboration is still limited. This demonstration showcases a novel prototype application that implements collaborative virtual environment and augmented reality technologies to improve remote collaborative process modelling, with an aim to assisting common collaboration tasks by providing an increased sense of immersion in an intuitive shared work and task space. Our tool is easily deployed using open source software, and commodity hardware, and is expected to assist with saving money on travel costs for large scale process modelling projects covering national and international centres within an enterprise.

Correlation of the expression of human kallikrein-related peptidases 4 and 7 with the prognosis in oral squamous cell carcinoma

Background Very few articles have been written about the expression of kallikreins (KLK4 and KLK7) in oral cancers. Therefore, the purpose of this study was to examine and report on their prognostic potential. Methods Eighty archival blocks of primary oral cancers were sectioned and stained for KLK4 and KLK7 by immunohistochemistry. The percentage and the intensity of malignant keratinocyte staining were correlated with patient survival using Cox regression analysis. Results Both kallikreins were expressed strongly in the majority of tumor cells in 68 of 80 cases: these were mostly moderately or poorly differentiated neoplasms. Staining was particularly intense at the infiltrating front. Patients with intense staining had significantly shorter overall survival (p < .05). Conclusion This is the first observation on the patient survival influenced by kallikrein expression in oral carcinoma. The findings are consistent with those for carcinomas at other sites, in particular the prostate and ovary. KLK4 and/or KLK7 immunohistochemistry seems to have diagnostic and prognostic potential in this disease.